import torch
import torch.nn as nn
from torchviz import make_dot
import torchsummary as ts
import hiddenlayer as hl
class ThreeDimensionAttention(nn.Module):
    def __init__(self,planes,ratio=16):
        super(ThreeDimensionAttention,self).__init__()
        self.ca=nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(planes,planes//ratio,1,stride=1,padding=0),
            nn.ReLU(),
            nn.Conv2d(planes//ratio,planes,1,stride=1,padding=0)
        )


        kernel_size=7
        self.sa=nn.Sequential(
            nn.Conv2d(planes,1,kernel_size,padding=(kernel_size-1)//2,bias=False),
         #   nn.BatchNorm2d(1,eps=1e-5,momentum=0.01,affine=True) ,
            nn.ReLU(),
        )
    
    def forward(self,x):
        b,c,h,w=x.shape

        xca=self.ca(x);xsa=self.sa(x)
        x=x.reshape(b,c,h,w)

        return x*torch.sigmoid(xca*xsa)





        
class BasicBlock(nn.Module):
    """Basic Block for resnet 18 and resnet 34

    """

    #BasicBlock and BottleNeck block
    #have different output size
    #we use class attribute expansion
    #to distinct
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()

        #residual function
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels * BasicBlock.expansion)
        )
        self.tda=ThreeDimensionAttention(out_channels*BasicBlock.expansion)

        #shortcut
        self.shortcut = nn.Sequential()

        #the shortcut output dimension is not the same with residual function
        #use 1*1 convolution to match the dimension
        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
            )

    def forward(self, x):
        return nn.ReLU(inplace=True)(self.tda(self.residual_function(x)) + self.shortcut(x))

class BottleNeck(nn.Module):
    """Residual block for resnet over 50 layers

    """
    expansion = 4
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels * BottleNeck.expansion),
        )
        self.tda=ThreeDimensionAttention(out_channels*BottleNeck.expansion)
        self.shortcut = nn.Sequential()

        if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
                nn.BatchNorm2d(out_channels * BottleNeck.expansion)
            )

    def forward(self, x):
        out=self.residual_function(x)
        out=self.tda(out)
        return nn.ReLU(inplace=True)(out + self.shortcut(x))

class ResNet(nn.Module):

    def __init__(self, block, num_block, num_classes=100):
        super().__init__()

        self.in_channels = 64

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True))
        #we use a different inputsize than the original paper
        #so conv2_x's stride is 1
        self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
        self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
        self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
        self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, num_blocks, stride):
        """make resnet layers(by layer i didnt mean this 'layer' was the
        same as a neuron netowork layer, ex. conv layer), one layer may
        contain more than one residual block

        Args:
            block: block type, basic block or bottle neck block
            out_channels: output depth channel number of this layer
            num_blocks: how many blocks per layer
            stride: the stride of the first block of this layer

        Return:
            return a resnet layer
        """

        # we have num_block blocks per layer, the first block
        # could be 1 or 2, other blocks would always be 1
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        output = self.conv1(x)
        output = self.conv2_x(output)
        output = self.conv3_x(output)
        output = self.conv4_x(output)
        output = self.conv5_x(output)
        output = self.avg_pool(output)
        output = output.view(output.size(0), -1)
        output = self.fc(output)

        return output

def resnet18():
    """ return a ResNet 18 object
    """
    return ResNet(BasicBlock, [2, 2, 2, 2])

def resnet34():
    """ return a ResNet 34 object
    """
    return ResNet(BasicBlock, [3, 4, 6, 3])

def resnet50():
    """ return a ResNet 50 object
    """
    return ResNet(BottleNeck, [3, 4, 6, 3])

def resnet101():
    """ return a ResNet 101 object
    """
    return ResNet(BottleNeck, [3, 4, 23, 3])

def resnet152():
    """ return a ResNet 152 object
    """
    return ResNet(BottleNeck, [3, 8, 36, 3])



models=resnet50()
x = torch.randn(1,3,224,224)

transforms = [
    # Fold Conv, BN, RELU layers into one
    hl.transforms.Fold("Conv > BatchNorm > Relu", "ConvBnRelu"),
    # Fold Conv, BN layers together
    hl.transforms.Fold("Conv > BatchNorm", "ConvBn"),
    # Fold bottleneck blocks
    hl.transforms.Fold("""
        ((ConvBnRelu > ConvBnRelu > ConvBn) | ConvBn) > Add > Relu
        """, "BottleneckBlock", "Bottleneck Block"),
    # Fold residual blocks
    hl.transforms.Fold("""ConvBnRelu > ConvBnRelu > ConvBn > Add > Relu""",
                       "ResBlock", "Residual Block"),
    # Fold repeated blocks
    hl.transforms.FoldDuplicates(),
]
#net_plot = make_dot(models(x),params = dict(models.named_parameters()))
#net_plot.view()

#ts.summary(models,(3,224,224))

hl.build_graph(models,torch.zeros([1,3,224,224]),transforms=transforms)